Queering feminist geography IV: toward queer and trans-feminist coalition
Bibliographic record
Abstract
In this fourth and final piece in our Queering Feminist Geography Viewpoint series, we turn toward the possibility and potentials of queer and trans-feminist coalition building. We reflect on work in our field that brings together queer, trans, and feminist perspectives and concepts by reviewing the historical relations between feminist and queer/trans thought in geography and assessing the present landscape of engagements between these realms. Seeking to build on these connections, we then present areas of intellectual and political commonality between feminist and queer/trans scholarship and praxis, outlining potential areas of further work. We close with our vision of queer and trans-feminist coalition and the obstacles that might get in the way of this vision.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.046 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".